1
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Villavicencio N, Groves MN. Tuning Reinforcement Learning Parameters for Cluster Selection to Enhance Evolutionary Algorithms. ACS ENGINEERING AU 2024; 4:381-393. [PMID: 39185391 PMCID: PMC11342372 DOI: 10.1021/acsengineeringau.3c00068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 08/27/2024]
Abstract
The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable intermediate structures being produced and increase the overall efficiency of an evolutionary algorithm, clustering was introduced in multiple instances. However, there is little literature detailing the effects of differentiating the selection frequencies between clusters. In order to find a balance between exploration and exploitation in our genetic algorithm, we propose a system of clustering the starting population and choosing clusters for an evolutionary algorithm run via a dynamic probability that is dependent on the fitness of molecules generated by each cluster. We define four parameters, MFavOvrAll-A, MFavClus-B, NoNewFavClus-C, and Select-D, that correspond to a reward for producing the best structure overall, a reward for producing the best structure in its own cluster, a penalty for not producing the best structure, and a penalty based on the selection ratio of the cluster, respectively. A reward increases the probability of a cluster's future selection, while a penalty decreases it. In order to optimize these four parameters, we used a Gaussian distribution to approximate the evolutionary algorithm performance of each cluster and performed a grid search for different parameter combinations. Results show parameter MFavOvrAll-A (rewarding clusters for producing the best structure overall) and parameter Select-D (appearance penalty) have a significantly larger effect than parameters MFavClus-B and NoNewFavClus-C. In order to produce the most successful models, a balance between MFavOvrAll-A and Select-D must be made that reflects the exploitation vs exploration trade-off often seen in reinforcement learning algorithms. Results show that our reinforcement-learning-based method for selecting clusters outperforms an unclustered evolutionary algorithm for quinoline-like structure searches.
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Affiliation(s)
- Nathan Villavicencio
- Department
of Mathematics, California State University
Fullerton, Fullerton, California 92834, United States
| | - Michael N. Groves
- Department
of Chemistry and Biochemistry, California
State University Fullerton, Fullerton, California 92834, United States
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2
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Ai C, Han S, Yang X, Vegge T, Hansen HA. Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing Pd xTi 1-xH y Surfaces under Various CO 2 Reduction Reaction Conditions. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38437157 DOI: 10.1021/acsami.3c18734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO2 reduction reaction (CO2RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of the CO2 reduction reaction compared with pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction conditions are linked to their stability, activity, and selectivity toward the CO2RR and HER, as shown in our previous work. In fact, various coverages, types, and mixtures of the adsorbates, as well as state variables such as temperature, pressure, applied potential, and chemical potential, could impact their stability, activity, and selectivity. However, these factors are usually fixed at common values to reduce the complexity of the structures and the complexity of the reaction conditions in most theoretical work. To address the complexities above and the huge search space, we apply a deep learning-assisted multitasking genetic algorithm to screen for PdxTi1-xHy surfaces containing multiple adsorbates for CO2RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and retain a high accuracy and low uncertainty of the energy and forces. The multitasking genetic algorithm simultaneously finds globally stable surface structures under each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among these, Pd0.56Ti0.44H1.06 + 25%CO, Pd0.31Ti0.69H1.25 + 50%CO, Pd0.31Ti0.69H1.25 + 25%CO, and Pd0.88Ti0.12H1.06 + 25%CO are found to be very active for CO2RR and suitable to generate syngas consisting of CO and H2.
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Affiliation(s)
- Changzhi Ai
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Shuang Han
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Xin Yang
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
| | - Heine Anton Hansen
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark
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3
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van der Oord C, Sachs M, Kovács DP, Ortner C, Csányi G. Hyperactive learning for data-driven interatomic potentials. NPJ COMPUTATIONAL MATERIALS 2023; 9:168. [PMID: 38666057 PMCID: PMC11041776 DOI: 10.1038/s41524-023-01104-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 08/02/2023] [Indexed: 04/28/2024]
Abstract
Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy surfaces. The most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training database. To aid this process hyperactive learning (HAL), an accelerated active learning scheme, is presented as a method for rapid automated training database assembly. HAL adds a biasing term to a physically motivated sampler (e.g. molecular dynamics) driving atomic structures towards uncertainty in turn generating unseen or valuable training configurations. The proposed HAL framework is used to develop atomic cluster expansion (ACE) interatomic potentials for the AlSi10 alloy and polyethylene glycol (PEG) polymer starting from roughly a dozen initial configurations. The HAL generated ACE potentials are shown to be able to determine macroscopic properties, such as melting temperature and density, with close to experimental accuracy.
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4
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Guo YX, Zhuang YB, Shi J, Cheng J. ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys. J Chem Phys 2023; 159:094801. [PMID: 37655767 DOI: 10.1063/5.0166858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/14/2023] [Indexed: 09/02/2023] Open
Abstract
Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1-xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials.
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Affiliation(s)
- Yu-Xin Guo
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yong-Bin Zhuang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jueli Shi
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jun Cheng
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
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5
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Shi X, Cheng D, Zhao R, Zhang G, Wu S, Zhen S, Zhao ZJ, Gong J. Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning. Chem Sci 2023; 14:8777-8784. [PMID: 37621421 PMCID: PMC10445438 DOI: 10.1039/d3sc02974c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/13/2023] [Indexed: 08/26/2023] Open
Abstract
The complex reconstructed structure of materials can be revealed by global optimization. This paper describes a hybrid evolutionary algorithm (HEA) that combines differential evolution and genetic algorithms with a multi-tribe framework. An on-the-fly machine learning calculator is adopted to expedite the identification of low-lying structures. With a superior performance to other well-established methods, we further demonstrate its efficacy by optimizing the complex oxidized surface of Pt/Pd/Cu with different facets under (4 × 4) periodicity. The obtained structures are consistent with experimental results and are energetically lower than the previously presented model.
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Affiliation(s)
- Xiangcheng Shi
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
- Department of Chemistry, National University of Singapore 3 Science Drive 3 Singapore 117543 Republic of Singapore
| | - Dongfang Cheng
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
| | - Ran Zhao
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
| | - Gong Zhang
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
| | - Shican Wu
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
| | - Shiyu Zhen
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
| | - Zhi-Jian Zhao
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- Haihe Laboratory of Sustainable Chemical Transformations Tianjin 300192 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
| | - Jinlong Gong
- School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University Tianjin 300072 China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin 300072 China
- Haihe Laboratory of Sustainable Chemical Transformations Tianjin 300192 China
- National Industry-Education Platform of Energy Storage, Tianjin University 135 Yaguan Road Tianjin 300350 China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University Binhai New City Fuzhou 350207 Fujian China
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6
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Lourenço MP, Hostaš J, Herrera LB, Calaminici P, Köster AM, Tchagang A, Salahub DR. GAMaterial-A genetic-algorithm software for material design and discovery. J Comput Chem 2023; 44:814-823. [PMID: 36444916 DOI: 10.1002/jcc.27043] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/26/2022] [Accepted: 11/06/2022] [Indexed: 11/30/2022]
Abstract
Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti6 O12 cluster, doping Al in Si11 (4Al@Si11 ) and Na10 supported on graphene (Na10 @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo8 C4 cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial.
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Affiliation(s)
- Maicon Pierre Lourenço
- Departamento de Química e Física - Centro de Ciências Exatas, Naturais e da Saúde - CCENS - Universidade Federal do Espírito Santo, Espírito Santo, Brazil
| | - Jiří Hostaš
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, Canada
| | - Lizandra Barrios Herrera
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, Canada
| | | | | | - Alain Tchagang
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, Ontario, Canada
| | - Dennis R Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, Canada
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7
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Kulichenko M, Barros K, Lubbers N, Li YW, Messerly R, Tretiak S, Smith JS, Nebgen B. Uncertainty-driven dynamics for active learning of interatomic potentials. NATURE COMPUTATIONAL SCIENCE 2023; 3:230-239. [PMID: 38177878 PMCID: PMC10766548 DOI: 10.1038/s43588-023-00406-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/24/2023] [Indexed: 01/06/2024]
Abstract
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions.
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Affiliation(s)
- Maksim Kulichenko
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Richard Messerly
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
- Nvidia Corporation, Santa Clara, CA, USA.
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
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8
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Exploring catalytic reaction networks with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-022-00896-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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9
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Morente-Molinera J, Morfeq A, Al-Hmouz R, Ashary E, Su J, Herrera-Viedma E. Introducing disruption on stagnated Group Decision Making processes using Fuzzy Ontologies. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Yao Z, Lum Y, Johnston A, Mejia-Mendoza LM, Zhou X, Wen Y, Aspuru-Guzik A, Sargent EH, Seh ZW. Machine learning for a sustainable energy future. NATURE REVIEWS. MATERIALS 2022; 8:202-215. [PMID: 36277083 PMCID: PMC9579620 DOI: 10.1038/s41578-022-00490-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/14/2022] [Indexed: 05/28/2023]
Abstract
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances - at the materials, devices and systems levels - for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.
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Affiliation(s)
- Zhenpeng Yao
- Shanghai Key Laboratory of Hydrogen Science & Center of Hydrogen Science, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yanwei Lum
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Innovis, Singapore, Singapore
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario Canada
| | - Andrew Johnston
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario Canada
| | - Luis Martin Mejia-Mendoza
- Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Xin Zhou
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yonggang Wen
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Edward H. Sargent
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario Canada
| | - Zhi Wei Seh
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Innovis, Singapore, Singapore
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11
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Christiansen MPV, Rønne N, Hammer B. Atomistic Global Optimization X: A Python package for optimization of atomistic structures. J Chem Phys 2022; 157:054701. [DOI: 10.1063/5.0094165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Modelling and understanding properties of materials from first principles require knowledge of the underlyingatomistic structure. This entails knowing the individual chemical identity and position of all atoms involved.Obtaining such information for macro-molecules, nano-particles, clusters, and for the surface, interface, andbulk phases of amorphous and solid materials represents a difficult high-dimensional global optimizationproblem. The rise of machine learning techniques in materials science has, however, led to many compellingdevelopments that may speed up structure searches. The complexity of such new methods has prompted aneed for an efficient way of assembling them into global optimization algorithms that can be experimentedwith. In this paper, we introduce the Atomistic Global Optimization X (AGOX) framework and code, asa customizable approach that enables efficient building and testing of global optimization algorithms. Amodular way of expressing global optimization algorithms is described and modern programming practicesare used to enable that modularity in the freely available AGOX python package. A number of examplesof global optimization approaches are implemented and analyzed. This ranges from random search andbasin-hopping to machine learning aided approaches with on-the-fly learnt surrogate energy landscapes. Themethods are show-cased on problems ranging from supported clusters over surface reconstructions to largecarbon clusters and metal-nitride clusters incorporated into graphene sheets.
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Affiliation(s)
| | - Nikolaj Rønne
- Aarhus University Department of Physics and Astronomy, Denmark
| | - Bjørk Hammer
- Department of Physics and Astronomy and Interdisciplinary Nanoscience Center (iNANO) and Department of Physics and Astronomy, Aarhus University Department of Physics and Astronomy, Denmark
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12
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Lourenço MP, Herrera LB, Hostaš J, Calaminici P, Köster AM, Tchagang A, Salahub DR. A new active learning approach for adsorbate-substrate structural elucidation in silico. J Mol Model 2022; 28:178. [PMID: 35654918 DOI: 10.1007/s00894-022-05173-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, such as finding the optimum adsorption geometry and energy, is challenging, due to the huge number of possibilities of assembling the adsorbate on the surface. In the current work, we have developed an artificial intelligence (AI) approach based on an active learning (AL) method for adsorption optimization on the surface of materials. AL uses machine learning (ML) regression algorithms and their uncertainties to make a decision (based on a policy) for the next unexplored structures to be computed, increasing, though, the probability of finding the global minimum with a small number of calculations. The methodology allows an accurate and automated structural elucidation of the adsorbate on the surface, based on the minimization of the total electronic energy. The new AL method for adsorption optimization was developed and implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial) program and was applied for C60@TiO2 anatase (101). It marks another software extension with a new feature in addition to the automatic structural elucidation of defects in materials and of nanoparticles as well. SCC-DFTB calculations were used to build the complex search surfaces with a reasonably low computational cost. An artificial neural network (NN) was employed in the AL framework evaluated together with two uncertainty quantification methods: K-fold cross-validation and non-parametric bootstrap (BS) resampling. Also, two different acquisition functions for decision-making were used: expected improvement (EI) and the lower confidence bound (LCB).
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Affiliation(s)
- Maicon Pierre Lourenço
- Departamento de Química e Física - Centro de Ciências Exatas, Naturais e da Saúde - CCENS - Universidade Federal do Espírito Santo, 29500-000, Alegre, Espírito Santo, Brasil.
| | - Lizandra Barrios Herrera
- Department of Chemistry, Department of Physics and Astronomy, Quantum Alberta, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Jiří Hostaš
- Department of Chemistry, Department of Physics and Astronomy, Quantum Alberta, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Patrizia Calaminici
- Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional 2508, AP 14-740, México City, D.F., 07000, México
| | - Andreas M Köster
- Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional 2508, AP 14-740, México City, D.F., 07000, México
| | - Alain Tchagang
- Digital Technologies Research Centre, National Research Council of Canada, 1200 Montréal Road, Ottawa, ON, K1A 0R6, Canada
| | - Dennis R Salahub
- Department of Chemistry, Department of Physics and Astronomy, Quantum Alberta, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
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13
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Bauer MN, Probert MIJ, Panosetti C. Systematic Comparison of Genetic Algorithm and Basin Hopping Approaches to the Global Optimization of Si(111) Surface Reconstructions. J Phys Chem A 2022; 126:3043-3056. [PMID: 35522778 PMCID: PMC9126620 DOI: 10.1021/acs.jpca.2c00647] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
![]()
We present a systematic
study of two widely used material structure
prediction methods, the Genetic Algorithm and Basin Hopping approaches
to global optimization, in a search for the 3 × 3, 5 × 5,
and 7 × 7 reconstructions of the Si(111) surface. The Si(111)
7 × 7 reconstruction is the largest and most complex surface
reconstruction known, and finding it is a very exacting test for global
optimization methods. In this paper, we introduce a modification to
previous Genetic Algorithm work on structure search for periodic systems,
to allow the efficient search for surface reconstructions, and present
a rigorous study of the effect of the different parameters of the
algorithm. We also perform a detailed comparison with the recently
improved Basin Hopping algorithm using Delocalized Internal Coordinates.
Both algorithms succeeded in either resolving the 3 × 3, 5 ×
5, and 7 × 7 DAS surface reconstructions or getting “sufficiently
close”, i.e., identifying structures that only differ for the
positions of a few atoms as well as thermally accessible structures
within kBT/unit area
of the global minimum, with T = 300 K. Overall, the
Genetic Algorithm is more robust with respect to parameter choice
and in success rate, while the Basin Hopping method occasionally exhibits
some advantages in speed of convergence. In line with previous studies,
the results confirm that robustness, success, and speed of convergence
of either approach are strongly influenced by how much the trial moves
tend to preserve favorable bonding patterns once these appear.
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Affiliation(s)
- Maximilian N Bauer
- Department of Physics, University of York, York YO10 5DD, United Kingdom.,Technical University of Munich, Lichtenbergstraße 4, 85748 Garching, Germany
| | - Matt I J Probert
- Department of Physics, University of York, York YO10 5DD, United Kingdom
| | - Chiara Panosetti
- Technical University of Munich, Lichtenbergstraße 4, 85748 Garching, Germany.,Fritz Haber Institute of the Max Planck Society, Faradayweg 4, 14195 Berlin, Germany
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14
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Lourenço MP, Herrera LB, Hostaš J, Calaminici P, Köster AM, Tchagang A, Salahub DR. Automatic structural elucidation of vacancies in materials by active learning. Phys Chem Chem Phys 2022; 24:25227-25239. [DOI: 10.1039/d2cp02585j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The artificial intelligence method based on active learning for the automatic structural elucidation of vacancies in materials. This is implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial).
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Affiliation(s)
- Maicon Pierre Lourenço
- Departamento de Química e Física – Centro de Ciências Exatas, Naturais e da Saúde – CCENS – Universidade Federal do Espírito Santo, 29500-000, Alegre, Espírito Santo, Brazil
| | - Lizandra Barrios Herrera
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Jiří Hostaš
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Patrizia Calaminici
- Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional 2508, AP 14-740, México D.F. 07000, Mexico
| | - Andreas M. Köster
- Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional 2508, AP 14-740, México D.F. 07000, Mexico
| | - Alain Tchagang
- Digital Technologies Research Centre, National Research Council of Canada, 1200 Montréal Road, Ottawa, ON, K1A 0R6, Canada
| | - Dennis R. Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
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15
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Cui J, Yang B, Sun B, Hu X, Liu J. Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:103-116. [PMID: 33048764 DOI: 10.1109/tnnls.2020.3027552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We propose a general and scalable global optimization framework directly operating on annotated graph data by introducing a Bayesian graph neural network to approximate the expensive-to-evaluate objectives. It prevents the cubical complexity of Gaussian processes and can scale linearly with the number of observations. Its parallelized variant makes it scalable. We provide strict theoretical support on its convergence. Intensive experiments conducted on both artificial and real-world problems, including molecular discovery and urban road network design, demonstrate the effectiveness of the proposed methods compared with the current state of the art.
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16
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Shi X, Lin X, Luo R, Wu S, Li L, Zhao ZJ, Gong J. Dynamics of Heterogeneous Catalytic Processes at Operando Conditions. JACS AU 2021; 1:2100-2120. [PMID: 34977883 PMCID: PMC8715484 DOI: 10.1021/jacsau.1c00355] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Indexed: 05/02/2023]
Abstract
The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.
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Affiliation(s)
- Xiangcheng Shi
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
- Joint
School of National University of Singapore and Tianjin University,
International Campus of Tianjin University, Fuzhou 350207, China
| | - Xiaoyun Lin
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Ran Luo
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Shican Wu
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Lulu Li
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Zhi-Jian Zhao
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
| | - Jinlong Gong
- Key
Laboratory for Green Chemical Technology of Ministry of Education,
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300072, China
- Joint
School of National University of Singapore and Tianjin University,
International Campus of Tianjin University, Fuzhou 350207, China
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17
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Allotey J, Butler KT, Thiyagalingam J. Entropy-based active learning of graph neural network surrogate models for materials properties. J Chem Phys 2021; 155:174116. [PMID: 34742215 DOI: 10.1063/5.0065694] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Graph neural networks trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks once trained are able to make highly accurate predictions at a fraction of the cost of experiments or first-principles calculations of comparable accuracy. However, these networks typically rely on large databases of labeled experiments to train the model. In scenarios where data are scarce or expensive to obtain, this can be prohibitive. By building a neural network that provides confidence on the predicted properties, we are able to develop an active learning scheme that can reduce the amount of labeled data required by identifying the areas of chemical space where the model is most uncertain. We present a scheme for coupling a graph neural network with a Gaussian process to featurize solid-state materials and predict properties including a measure of confidence in the prediction. We then demonstrate that this scheme can be used in an active learning context to speed up the training of the model by selecting the optimal next experiment for obtaining a data label. Our active learning scheme can double the rate at which the performance of the model on a test dataset improves with additional data compared to choosing the next sample at random. This type of uncertainty quantification and active learning has the potential to open up new areas of materials science, where data are scarce and expensive to obtain, to the transformative power of graph neural networks.
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Affiliation(s)
- Johannes Allotey
- School of Physics, University of Bristol, Bristol BS8 1TL, United Kingdom
| | - Keith T Butler
- Scientific Machine Learning Research Group, Scientific Computing Department, Rutherford Appleton Laboratory, Science and Technology Facilities Council, Didcot OX11 0DQ, United Kingdom
| | - Jeyan Thiyagalingam
- Scientific Machine Learning Research Group, Scientific Computing Department, Rutherford Appleton Laboratory, Science and Technology Facilities Council, Didcot OX11 0DQ, United Kingdom
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18
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Abstract
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.
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Affiliation(s)
- Bing Huang
- Faculty
of Physics, University of Vienna, 1090 Vienna, Austria
| | - O. Anatole von Lilienfeld
- Faculty
of Physics, University of Vienna, 1090 Vienna, Austria
- Institute
of Physical Chemistry and National Center for Computational Design
and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, 4056 Basel, Switzerland
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19
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Lourenço MP, Herrera LB, Hostaš J, Calaminici P, Köster AM, Tchagang A, Salahub DR. Taking the multiplicity inside the loop: active learning for structural and spin multiplicity elucidation of atomic clusters. Theor Chem Acc 2021. [DOI: 10.1007/s00214-021-02820-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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20
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Enhancing crystal structure prediction by decomposition and evolution schemes based on graph theory. FUNDAMENTAL RESEARCH 2021. [DOI: 10.1016/j.fmre.2021.06.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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21
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An adaptive hybrid differential evolution algorithm for continuous optimization and classification problems. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06216-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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22
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23
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Garijo del Río E, Kaappa S, Garrido Torres JA, Bligaard T, Jacobsen KW. Machine learning with bond information for local structure optimizations in surface science. J Chem Phys 2020; 153:234116. [DOI: 10.1063/5.0033778] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
| | - Sami Kaappa
- Department of Physics, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - José A. Garrido Torres
- SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, California 94305, USA
- SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
- Columbia Electrochemical Energy Center, Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
| | - Thomas Bligaard
- SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, California 94305, USA
- Department of Energy Conversion and Storage, Technical University of Denmark, Kgs. Lyngby, Denmark
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24
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Christiansen MPV, Mortensen HL, Meldgaard SA, Hammer B. Gaussian representation for image recognition and reinforcement learning of atomistic structure. J Chem Phys 2020; 153:044107. [DOI: 10.1063/5.0015571] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | | | - Søren Ager Meldgaard
- Department of Physics and Astronomy, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Bjørk Hammer
- Department of Physics and Astronomy, Aarhus University, DK-8000 Aarhus C, Denmark
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25
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del Rosario Z, Rupp M, Kim Y, Antono E, Ling J. Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization. J Chem Phys 2020; 153:024112. [DOI: 10.1063/5.0006124] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Zachary del Rosario
- Olin College of Engineering, 1000 Olin Way, Needham, Massachusetts 02492, USA
| | - Matthias Rupp
- Citrine Informatics, 2629 Broadway, Redwood City, California 94063, USA
| | - Yoolhee Kim
- Citrine Informatics, 2629 Broadway, Redwood City, California 94063, USA
| | - Erin Antono
- Citrine Informatics, 2629 Broadway, Redwood City, California 94063, USA
| | - Julia Ling
- Citrine Informatics, 2629 Broadway, Redwood City, California 94063, USA
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26
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An adaptive design approach for defects distribution modeling in materials from first-principle calculations. J Mol Model 2020; 26:187. [PMID: 32613379 DOI: 10.1007/s00894-020-04438-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 06/03/2020] [Indexed: 10/23/2022]
Abstract
Designing and understanding the mechanism of non-stoichiometric materials with enhanced properties is challenging, both experimentally and even computationally, due to the large number of chemical spaces and their distributions through the material. In the current work, it is proposed a Machine Learning approach coupled with the Efficient Global Optimization (EGO) method-an Adaptive Design (AD)-to model local defects in materials from first-principle calculations. Our method takes into account the smallest sample set as possible, envisioning the material defect structure relationship with target properties for new insights. As an example, the AD framework allows us to study the stability and the structure of the modified goethite (Fe0.875Al0.125OOH) by considering a proper defect distribution, from first-principle calculations. The chemical space search for the modified goethite was evaluated by starting from different sizes and configurations of the samples as well as different surrogate models (ANN and Gaussian Process; GP), acquisition functions, and descriptors. Our results show that the same local solution of several defect arrangements in Fe0.875Al0.125OOH is found regardless of the initial sample and regression model. This indicates the efficiency of our search method. We also discuss the role of the descriptors in the accelerated global search for defects in material modeling. We conclude that the AD method applied in material defects is a successful approach in automating the search within huge chemical spaces from first-principle calculations by considering small samples. This method can be applied to mechanistic elucidation of non-stoichiometric materials, solid solutions, alloys, and Schottky and Frenkel defects, essential for material design and discovery. Graphical abstract.
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27
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von Lilienfeld OA, Müller KR, Tkatchenko A. Exploring chemical compound space with quantum-based machine learning. Nat Rev Chem 2020; 4:347-358. [PMID: 37127950 DOI: 10.1038/s41570-020-0189-9] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2020] [Indexed: 12/16/2022]
Abstract
Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space - the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.
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28
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Zhang J, Chen J, Hu P, Wang H. Identifying the composition and atomic distribution of Pt-Au bimetallic nanoparticle with machine learning and genetic algorithm. CHINESE CHEM LETT 2020. [DOI: 10.1016/j.cclet.2019.12.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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29
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Järvi J, Rinke P, Todorović M. Detecting stable adsorbates of (1 S)-camphor on Cu(111) with Bayesian optimization. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2020; 11:1577-1589. [PMID: 33134002 PMCID: PMC7590619 DOI: 10.3762/bjnano.11.140] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 09/16/2020] [Indexed: 05/08/2023]
Abstract
Identifying the atomic structure of organic-inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among eight unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, such as BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.
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Affiliation(s)
- Jari Järvi
- Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland
| | - Patrick Rinke
- Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland
| | - Milica Todorović
- Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland
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30
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Sharipov AS, Loukhovitski BI. Small atomic clusters: quantum chemical research of isomeric composition and physical properties. Struct Chem 2019. [DOI: 10.1007/s11224-019-01417-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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31
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Schlexer Lamoureux P, Winther KT, Garrido Torres JA, Streibel V, Zhao M, Bajdich M, Abild‐Pedersen F, Bligaard T. Machine Learning for Computational Heterogeneous Catalysis. ChemCatChem 2019. [DOI: 10.1002/cctc.201900595] [Citation(s) in RCA: 144] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Philomena Schlexer Lamoureux
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Kirsten T. Winther
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Jose Antonio Garrido Torres
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Verena Streibel
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Meng Zhao
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Michal Bajdich
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Frank Abild‐Pedersen
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Thomas Bligaard
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
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32
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Garrido Torres JA, Jennings PC, Hansen MH, Boes JR, Bligaard T. Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model. PHYSICAL REVIEW LETTERS 2019; 122:156001. [PMID: 31050513 DOI: 10.1103/physrevlett.122.156001] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Indexed: 06/09/2023]
Abstract
We present the incorporation of a surrogate Gaussian process regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical nudged elastic band (NEB) calculations. In our surrogate model approach, the cost of converging the elastic band no longer scales with the number of moving images on the path. This provides a far more efficient and robust transition state search. In contrast to a conventional NEB calculation, the algorithm presented here eliminates any need for manipulating the number of images to obtain a converged result. This is achieved by inventing a new convergence criteria that exploits the probabilistic nature of the GPR to use uncertainty estimates of all images in combination with the force in the saddle point in the target model potential. Our method is an order of magnitude faster in terms of function evaluations than the conventional NEB method with no accuracy loss for the converged energy barrier values.
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Affiliation(s)
- José A Garrido Torres
- Stanford University, Department of Chemical Engineering, Stanford, California 94305, USA
- SUNCAT Center for Interface Science and Catalysis, Stanford Linear Accelerator Center, Menlo Park, California 94025, USA
| | - Paul C Jennings
- Stanford University, Department of Chemical Engineering, Stanford, California 94305, USA
- SUNCAT Center for Interface Science and Catalysis, Stanford Linear Accelerator Center, Menlo Park, California 94025, USA
| | - Martin H Hansen
- Stanford University, Department of Chemical Engineering, Stanford, California 94305, USA
- SUNCAT Center for Interface Science and Catalysis, Stanford Linear Accelerator Center, Menlo Park, California 94025, USA
| | - Jacob R Boes
- Stanford University, Department of Chemical Engineering, Stanford, California 94305, USA
- SUNCAT Center for Interface Science and Catalysis, Stanford Linear Accelerator Center, Menlo Park, California 94025, USA
| | - Thomas Bligaard
- SUNCAT Center for Interface Science and Catalysis, Stanford Linear Accelerator Center, Menlo Park, California 94025, USA
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33
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Grajciar L, Heard CJ, Bondarenko AA, Polynski MV, Meeprasert J, Pidko EA, Nachtigall P. Towards operando computational modeling in heterogeneous catalysis. Chem Soc Rev 2018; 47:8307-8348. [PMID: 30204184 PMCID: PMC6240816 DOI: 10.1039/c8cs00398j] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Indexed: 12/19/2022]
Abstract
An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.
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Affiliation(s)
- Lukáš Grajciar
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Christopher J. Heard
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Anton A. Bondarenko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Mikhail V. Polynski
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Jittima Meeprasert
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Evgeny A. Pidko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Petr Nachtigall
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
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34
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Sørensen KH, Jørgensen MS, Bruix A, Hammer B. Accelerating atomic structure search with cluster regularization. J Chem Phys 2018; 148:241734. [DOI: 10.1063/1.5023671] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- K. H. Sørensen
- Department of Physics and Astronomy, and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, DK-8000 Aarhus C, Denmark
| | - M. S. Jørgensen
- Department of Physics and Astronomy, and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, DK-8000 Aarhus C, Denmark
| | - A. Bruix
- Department of Physics and Astronomy, and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, DK-8000 Aarhus C, Denmark
| | - B. Hammer
- Department of Physics and Astronomy, and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, DK-8000 Aarhus C, Denmark
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Chen X, Jørgensen MS, Li J, Hammer B. Atomic Energies from a Convolutional Neural Network. J Chem Theory Comput 2018; 14:3933-3942. [DOI: 10.1021/acs.jctc.8b00149] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Xin Chen
- Department of Chemistry and Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education, Tsinghua University, Beijing 100084, China
- Interdisciplinary Nanoscience Center (iNANO) and Department of Physics and Astronomy, Aarhus University, Aarhus DK-8000, Denmark
| | - Mathias S. Jørgensen
- Interdisciplinary Nanoscience Center (iNANO) and Department of Physics and Astronomy, Aarhus University, Aarhus DK-8000, Denmark
| | - Jun Li
- Department of Chemistry and Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education, Tsinghua University, Beijing 100084, China
| | - Bjørk Hammer
- Interdisciplinary Nanoscience Center (iNANO) and Department of Physics and Astronomy, Aarhus University, Aarhus DK-8000, Denmark
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